Search alternatives:
resource » source (Expand Search)
resources » sources (Expand Search)
functions » function (Expand Search)
Showing 661 - 680 results of 1,810 for search '((( resource OR resources) detection functions ) OR ( sources detection functions ))', query time: 0.30s Refine Results
  1. 661

    Nonuniform Doppler extraction-enhanced multichannel extensive cancellation algorithm for passive radar using Iridium satellite signals by Hongwei Fu, Zhang Zhang, Yu Luo, Tingting Fu, Jianglin Li, Xin Jian, Hao Cha

    Published 2025-08-01
    “…Abstract Passive radar (PR) relies on receiving signals reflected from targets by other existing noncooperative radiation sources, which are broadly divided into ground- and space-based categories, to achieve target detection and tracking. …”
    Get full text
    Article
  2. 662
  3. 663

    HGCS-Det: A Deep Learning-Based Solution for Localizing and Recognizing Household Garbage in Complex Scenarios by Houkui Zhou, Chang Chen, Zhongyi Xia, Qifeng Ding, Qinqin Liao, Qun Wang, Huimin Yu, Haoji Hu, Guangqun Zhang, Junguo Hu, Tao He

    Published 2025-06-01
    “…Notably, HGCS-Det maintains a lightweight architecture while enhancing the detection accuracy, enabling real-time performance even in resource-constrained environments. …”
    Get full text
    Article
  4. 664

    Enhancement of Corn Flour with Carob Bean for Innovative Gluten-Free Extruded Products by Marta Igual, Rosa M. Cámara, Francesca Fortuna, Patricia García-Herrera, Mercedes M. Pedrosa, Purificación García-Segovia, Javier Martínez-Monzó, Montaña Cámara

    Published 2024-10-01
    “…The aim of this work is to study new, extruded products based on corn flour enriched with carob bean and the evaluation of its functional quality to develop novel gluten-free food products. …”
    Get full text
    Article
  5. 665

    AGW-YOLO-Based UAV Remote Sensing Approach for Monitoring Levee Cracks by HU Weibo, ZHOU Shaoliang, ZHAO Erfeng, ZHAO Xueqiang

    Published 2025-01-01
    “…The method aims to ensure high detection accuracy with less computing cost. It enables real-time recognition of the geographic coordinates of cracks, thus providing technical support for the safety monitoring and subsequent maintenance of water conservancy projects.MethodsTo address challenges such as the difficulty in capturing subtle features and the limitations in computational resources during UAV-based levee crack inspection tasks, the AGW-YOLO object detection model was introduced. …”
    Get full text
    Article
  6. 666
  7. 667

    Deploying Android-Based Smart RSUs with YOLOv8 and SAHI for Enhanced Traffic Management by Mohammed F. Rashad, Qutaiba I. Ali

    Published 2025-03-01
    “…Future research will integrate functionalities like pedestrian detection and vehicle tracking to further enhance smart transportation systems. …”
    Get full text
    Article
  8. 668

    COMIX: Generalized Conflict Management in O-RAN xApps—Architecture, Workflow, and a Power Control Case by Anastasios E. Giannopoulos, Sotirios T. Spantideas, George Levis, Alexandros S. Kalafatelis, Panagiotis Trakadas

    Published 2025-01-01
    “…While this study considers power control xApps, the COMIX framework is generalizable and can be applied to any xApp conflict scenario involving resource contention or KPI interdependence.…”
    Get full text
    Article
  9. 669
  10. 670

    An Embodied Intelligence System for Coal Mine Safety Assessment Based on Multi-Level Large Language Models by Yi Sun, Faxiu Ji

    Published 2025-01-01
    “…By leveraging the tool invocation and reasoning capabilities of LLM in conjunction with a coal mine safety knowledge base, the system achieves logical inference, anomalous data detection, and potential safety risk prediction. Furthermore, its memory functionality ensures the learning and utilization of historical experiences, providing a solid foundation for continuous assessment processes. …”
    Get full text
    Article
  11. 671

    The Impact of Differences in Renovation Models of Abandoned Boiler Rooms on Community Vitality—A Case Study of Shenyang, China by Lei Chen, Yahang Cheng, Zixi Zhou, Yibo Wen

    Published 2025-05-01
    “…Unlike prior studies that rely on single data sources or unidimensional metrics, our multi-source approach enhances spatiotemporal resolution, improves the accuracy of subjective perceptions, and enables cross-validation between objective behavioral trajectories and residents’ self-reports, thereby significantly strengthening the comprehensiveness and reliability of community vitality measurement. …”
    Get full text
    Article
  12. 672

    SynergyBug: A deep learning approach to autonomous debugging and code remediation by Hong Chen

    Published 2025-07-01
    “…The system demonstrated exceptional detection strength for functional and performance, and security bugs, where the detection rates reached 94% and 90% and 92%, respectively. …”
    Get full text
    Article
  13. 673

    Terrain and individual tree vertical structure-based approach for point clouds co-registration by UAV and Backpack LiDAR by Tingwei Zhang, Xin Shen, Lin Cao

    Published 2025-05-01
    “…Tree-level structural parameters estimation plays a key role in the researches and practice in sustainable forest management, carbon storage estimation, as well as ecological function evaluation. However, single Light Detection and Ranging (LiDAR) platform exhibits limitations when acquiring complete (i.e., including over-story and under-story) point cloud data for forest stands, e.g., UAV LiDAR systems tend to overlook details of the tree trunk or the lower ground, while Backpack LiDAR systems struggle to capture the treetop, etc. …”
    Get full text
    Article
  14. 674

    Multi-model deep learning approach for the classification of kidney diseases using medical images by Waleed Obaid, Abir Hussain, Tamer Rabie, Dhafar Hamed Abd, Wathiq Mansoor

    Published 2025-01-01
    “…This research paper leveraged a deep learning approach to address the worldwide shortage of urologists by facilitating the detection of kidney diseases. A novel deep learning technique is proposed using Darknet53 for the classification of kidney diseases using a large dataset gathered from five resources. …”
    Get full text
    Article
  15. 675

    Molecular signature of selective microRNAs in Cyprinus carpio (Linnaeus 1758):a computational approach by Soumendu Ghosh, Manojit Bhattacharya, Avijit Kar, Basanta Kumar Das, Bidhan Chandra Patra

    Published 2019-03-01
    “…Their conserved nature in various organisms provide a good source of miRNA identification and characterization using comparative genomic approaches through the bio-computational tools. …”
    Get full text
    Article
  16. 676

    Multi-Robot Cooperative Simultaneous Localization and Mapping Algorithm Based on Sub-Graph Partitioning by Wan Xu, Yanliang Chen, Shijie Liu, Ao Nie, Rupeng Chen

    Published 2025-05-01
    “…First, a global matching and candidate loop selection strategy is incorporated into the front-end loop detection module, leveraging both LiDAR point clouds and visual features to achieve cross-robot loop detection, effectively mitigating computational redundancy and reducing false matches in collaborative multi-robot systems. …”
    Get full text
    Article
  17. 677

    Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey by Yizhe Wang, Ruifa Luo, Xiaoguang Yang

    Published 2025-06-01
    “…The construction of multi-source data fusion frameworks enables effective complementarity between ETC data, floating car data, and video detection data, significantly improving traffic state estimation accuracy. …”
    Get full text
    Article
  18. 678

    LiSA-MobileNetV2: an extremely lightweight deep learning model with Swish activation and attention mechanism for accurate rice disease classification by Yongqi Xu, Dongcheng Li, Changcheng Li, Zheming Yuan, Zhijun Dai

    Published 2025-08-01
    “…LiSA-MobileNetV2 provides a high-accuracy, resource-efficient solution for real-time rice disease detection in smart farming systems.…”
    Get full text
    Article
  19. 679
  20. 680